© 2018 MHM Innovations, Inc.

Data Science

"Data Science is more than just a tool, it is a paradigm shift" - Holly A. Russo, PhD - MHM Principal Scientist

 

Realizing the full benefit of data science requires a new mindset encompassing not only technology but acquisition, information assurance, professional development and leadership. Applying your traditional business model or concept of operations to your data science environment will most likely stall the evolution that could benefit your mission. Each organization has different architecture, staff, workflows, culture and missions that all will affect the success or failure of a data science effort.

Data Science done poorly can result in costly damage

An organization can overspend on technology, initiate tasks that become perpetual research projects, hire the wrong level of staff with the wrong skills, or introduce dangers to the internal network through open-source code or tool downloads. Worse yet, all of these things might be done right, but leadership either doesn't know how to make use of the data science products, or is overly-restrictive of data science activities, resulting in a failed environment. Finally, a lack of critical evaluation of data science products can result in bad decisions based on misleading results.

MHM's approach to Data Science:

Data Scientist versus Data Science Strategist and what sets MHM apart

MHM provides Data Science Strategists who have experience with prior data science environments from a management perspective, as well as extensive knowledge of data science technology. MHM Data Science Strategists:

  • Take a holistic view of an organization and how a data science capability fits into its strategy;

  • Maintain broad knowledge of technology, particularly what is in use by the data science community ;

  • Bridge the communication gap between leadership, IT, data scientists and other stakeholders;

  • Diagnose current data science workflows and capabilities that don't seem to be delivering their potential value, and recommend ways to improve;

  • Where needed, provide initial training on data science topics to leadership and stakeholders, and recommend sources for continued free, low-cost or traditional training.